This invention relates to method, procedure, algorithm, and computer program for improving and attempting to optimize the performance of marketing campaigns in which advertisements or other messages are distributed over an interactive measurable medium such as the Internet. When the message is an advertisement, the campaign involves a list of ad alternatives and a target customer population. The goal of the marketer is to allocate the ad alternatives to the customer population to optimize business objectives such as maximizing the number of responses received. When the message is other than an advertisement, the goal is to otherwise allocate messages to optimize analogous business message or other message campaign objectives, typically measured by the number of successes or successful responses. In this description, the term “ad” has the same meaning and is used interchangeably with the term “advertisement”.
In large part due to the particular applicability of the invention to advertisements on the Internet, this background description focuses on Internet advertising to establish one context of the invention and to differentiate the invention from conventional systems and methods. It is to be understood however, that the invention is not to be interpreted to be limited only to an Internet advertising environment or to advertising environments alone no matter what the media, rather the invention pertains to a broad spectrum of message and messaging contexts in or on various interactive media.
Various systems currently exist for the delivery and tracking of advertisements on the Internet, for instance, ad servers for serving and tracking “banner ads” on a web page. The users of these ad delivery or ad server systems have access to data on the performance of all the ads on all the locations. This data is updated by the delivery and tracking system on a periodic basis. The users are also provided with an array of parameters to configure the delivery and tracking system. In a typical conventional situation, an advertiser buys advertising space (ad space) on a number of web sites. The advertising space buy on each web site consists of a number of categories. Such categories may correspond to different sections within that web site, where a section is a specific web page or a set of related web pages within the site. A category may also correspond to keywords searched by a customer on a search engine. The term “zone” will be used to represent a unique site and category combination. There may typically be a number of banners that an advertiser wishes to deploy across these zones. A banner is either a graphic image that announces the name or identity of a site or is an advertising image. An impression occurs when an Internet visitor sees a banner. A clickthrough occurs when a visitor to a zone clicks on a banner. This redirects the visitor to the page on the advertiser's web site.
The fraction of impressions that should be allocated to a particular banner alternative for a zone is an important parameter that the advertiser (or other messaging entity) can select and modify to boost the advertising campaign performance.
Impressions can occur at any time—whenever someone visits the appropriate page of a web site. However, the reports are typically updated at discrete times. We will call the intermediate time between two reports a stage. At the end of each stage, the results are available for that stage. In particular, the following information is available for each banner for a given zone: (1) the number of impressions delivered during a stage, and (2) the number of clickthroughs generated during a stage.
Additionally this information (that is, the number of impressions delivered during any particular stage or stages, and number of clickthroughs generated during any particular stage or stages, and the like) may be available separately for different characteristics of the visitor population. When a visitor arrives at a website, a variety of visitor profiling information may be available. This information may include, for example:                Data based on the current visit. Examples of this type of profiling information include the time of the visit, the type of browser used by the visitor, and the IP address.        Data based on an earlier visit. An example is data from a registration form that was filled out by the visitor on an earlier visit.        Data from external sources. For example, an external customer database may provide data on the purchase history of the visitor.Profile information in each list above is exemplary and not intended to be exhaustive. The data for each profiling attribute provides an opportunity for customizing the ad banner or other message shown to each campaign visitor. By selecting different banners or other messages for different visitor profiles, the overall ad or message campaign performance can be improved.        
In one embodiment, visitors can be classified into market segments based on this data. For example, segments based on age or income might be defined. The inventive method and algorithms can be used in conjunction with this type of market segmentation process. In particular, the invention can be applied to each segment independently. When a visitor arrives, he/she is assigned to a segment. Then the invention restricted to that segment is applied to the visitor.
This aspect of the invention relates to an algorithm to improve an advertising campaign's performance by dividing the space of customer population characteristics into multiple segments. Visitors belonging to one segment may, for example, be shown different ads or presented with different messages than visitors belonging to other segments. Segments may be identified based on measured response of visitors to different advertisements, messages, or other content.
An exemplary scenario illustrating the opportunity to customize the ad or other message is now described by way of example. This exemplary data pertains to a test campaign involving thirty banners. This data is sorted by the home states of the visitors. Using this data, the click-through rate of each banner in each of twenty-one states is estimated. (In this particular test data scenario, too little data are available to estimate click-through rates for all thirty banners in the remaining states). Therefore, for each of the twenty-one states one can identify the banner that achieved the highest click-through rate. The results of this scenario are summarized as a matrix of State versus banner in FIG. 4. Here rows correspond to the twenty-one states and columns to the thirty banners. The best banner in each state is highlighted. This figure illustrates that in general, different banners are preferred in different states. In particular, no one banner is best in all the states (or even in half of the states). Yet, if one were to ignore the home states of the visitors during this campaign, one would be forced to serve the same set of banners in all states. In this example the improvement that can be obtained by using the best banner for each state as opposed to the best overall banner is approximately 60%. This suggests that one can obtain significant performance improvements by using visitor-profiling data.
Ad servers or message servers generate reports that provide information about the impressions and clicks for different banners for one or more visitor attributes. These reports are provided in printed form or in the electronic equivalent of printed form, and are manually analyzed by trained analysis personnel to derive new, improved advertisement configurations. For example, they are analyzed in an attempt to optimize the clickthroughs generated by a pool of banner alternatives for a given zone, a given frequency level, and the like configuration information. This manual process is tedious and error-prone and has an inherent delay between the period of data collection and the time new advertisements are to be placed because of the large amount of data to be analyzed and the large number of parameters to be modified. Even if errors are not made and the user is able to overcome the tedium of the process, it is unlikely to yield optimal or even near-optimal recommendations for advertisement configurations. This is especially true in light of the typical delay of from a day to a week that elapses between data collection, analysis, and a new or modified ad campaign based on the analysis in conventional systems and methods.
Optimization to provide an effective advertising campaign is in essence a multi-dimensional optimization problem but that involves much more, that by-and-large cannot be timely solved using conventional tools, methods, or systems. It is noted that these problems exist substantially independent of the type of advertisement or message, and that such issues and problems exist relative to advertisements for products and services, political campaigns, ballot measures and initiatives, media programming, lobbying, surveys, polling, news headlines, sports scores, as well as other directed marketing, promotions, surveys, news, information, other content generally, and the like.
Therefore, there remains a need for an automated system for optimizing allocation parameters for advertisement alternatives or message alternatives. There also remains a need for an automated system and method for rapidly and efficiently executing the optimized allocation parameters to place the advertisement or message on the Internet or other local or global communication system. More particularly there remains a need for an optimization procedure or algorithm that utilizes available message performance information (for example, ad performance information) and generates recommendations for maintaining good performance or for improving performance during a subsequent stage of the campaign or optimizing performance of the entire campaign.
There also remains a need for a system and method that can learn and optimize across the various other parameters that can be reconfigured in advertisement delivery systems also commonly referred to as ad servers. For example, there remains a need for an ad server system and method that permits an advertiser to display different banners (or other content or messages) based on a time-of-day user web browsing profile which may include geographic location information, demographic information, or the like, as well as other user targeting information.
There also remains a need for an operating model that provides the optimized allocations for banner ad alternatives or message alternatives automatically on an interconnected network of computers or other information devices or appliances without significant human intervention.
These and other needs in conventional systems and methods are solved by the inventive system and method, particularly by the inventive optimization method and algorithm and computer software implementations of the inventive optimization algorithm and method.